A Survey on Data Selection for LLM Instruction Tuning What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning Does Fine-Tuning LLMs on New Knowledge Encourage
MoDS: Model-oriented Data Selection for Instruction Tuning From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning(IFD) Self-Evolved Diverse Data Sampling for Efficient Instruction Tuning(DiverseEvol)这些论文提供了丰富的见解和策略,帮助你在大模型的SFT阶段...
第一篇工作:WHAT MAKES GOOD DATA FOR ALIGNMENT? A COMPREHENSIVE STUDY OF AUTOMATIC DATA SELECTION IN INSTRUCTION TUNING 本文提出DEITA来自动选择SFT数据微调LLAMA和Mistral模型,目的是用更小的数据量实现更好的微调效果,最终文章选择了6k SFT微调数据和10k DPO数据。 核心观点: 所有知识是从预训练阶段获得的,SFT...
from sklearn.model_selection import train_test_split from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense # 数据划分 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 模型构建 model = Sequential() model.add(...
data['sentence'] = data['sentence'].apply(preprocess_text) 1. 2. 3. 4. 5. 6. 7. 8. 步骤4:数据集划分 将标注好的数据集划分为训练集、验证集和测试集。通常的比例是8:1:1。 from sklearn.model_selection import train_test_split
import pandas as pd from sklearn.model_selection import train_test_split # 加载或创建数据集 data = pd.read_csv('sft_dataset.csv') # 假设已有一个CSV格式的数据集 data = data[['instruction', 'answer']] # 确保数据集包含指令和答案两列 # 数据清洗与处理 data = data.dropna() # 删除含有缺...
Finally, questions and answers were combined and underwent rule-based filtering, detoxification, decontamination, quality checking, and ablation selection to produce this dataset. For more detailed information on the construction process, please refer to our technique report. Citation Information Please ...
transfer options as supported by the American Express network. While client partners may choose a product not included on the examples listed for each standard protocol, American Express cannot guarantee success of the implementation or support any special needs resulting from the product sel...
在实现过程中,ES参考一篇名为 C3的论文: Cutting Tail Latency in Cloud Data Stores via Adaptive Replica Selection ( https://www.usenix.org/conference/nsdi 15/technical-sessions/presentation/suresh)。这篇论文是为Cassandra写的,ES基于这篇论文的思想做了调整以适合自己的场景。
5. 3进入主菜单选择测井设置(Logging Setup),然后进入服务选择(Service Selection),选择 服务&仪器设置(Service &Tool Configurat 21、ion),若输入SRV2640建立裸眼井地层测试器的仪器串: 若输入SRV2650,建立套管井地层测试器的仪器串:(注意输入正确的仪器序列号)DITSHDD4TGQ/SY DQXXXX-2005GR D4SFT4CHSTRAINSFT4...